changed api structure and added object detection

api/src/server.py

@@ -0,0 +1,150 @@
+from enum import Enum
+import os
+import sqlite3
+
+from dotenv import load_dotenv
+from pydantic import BaseModel
+import torch
+from torchvision import transforms
+from fastapi import Depends, FastAPI, File, HTTPException, Header, UploadFile
+from fastapi.middleware.cors import CORSMiddleware
+from PIL import Image
+import io
+import torch.nn.functional as F
+
+from bird_cnn.bird_cnn import Bird_CNN
+
+import threading
+
+BUILD_PATH = "../build_models"
+IMAGE_SIZE = 64
+
+sem_ai = threading.Semaphore(1)
+
+class bird_species(Enum):
+    Common_Kingfisher = 0
+    Common_Myna = 1
+    House_Crow = 2
+    Indian_Peacock = 3
+    Indian_Pitta = 4
+    Ruddy_Shelduck = 5
+    Sarus_Crane = 6
+
+transform = transforms.Compose([
+    transforms.Resize(IMAGE_SIZE),
+    transforms.CenterCrop(IMAGE_SIZE),
+    transforms.ToTensor()
+])
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+model = Bird_CNN(c_in=3, c_hidden=16, c_out=7)
+full_path = os.path.join(BUILD_PATH, "bird_cnn")
+model.load_state_dict(torch.load(full_path, map_location=torch.device(device)))
+model.to(device)
+model.eval()
+
+app = FastAPI()
+
+origins = [
+    "https://marvinkrausser.com",
+    "https://api.marvinkrausser.com",
+]
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+@app.post("/predict")
+async def predict(file: UploadFile = File(...)):
+    with sem_ai:
+        image_bytes = await file.read()
+
+        image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+        image = transform(image).unsqueeze(0).to(device)
+
+        with torch.no_grad():
+            pred = model(image)
+            probs = F.softmax(pred, dim=1)
+            confidence, cls = torch.max(probs, dim=1)
+
+        return {
+            "class": bird_species(cls.item()).name,
+            "confidence": confidence.item()
+        }
+
+
+load_dotenv("../database/.env")
+API_KEY = os.getenv("API_KEY")
+
+def get_api_key(authorization: str = Header(None)):
+    if authorization != f"Bearer {API_KEY}":
+        raise HTTPException(status_code=401, detail="Unauthorized")
+    return authorization
+
+class Review(BaseModel):
+    website: str
+    rating: int
+    text: str
+    date: str
+
+sem_db = threading.Semaphore(1)
+
+@app.get("/review")
+def get_reviews(auth=Depends(get_api_key)):
+    with sem_db:
+        conn = sqlite3.connect(
+            "database/reviews.db",
+            check_same_thread=False
+        )
+        cur = conn.cursor()
+
+        cur.execute("""
+        SELECT * FROM reviews;
+        """)
+
+        data = cur.fetchall()
+
+        print(data)
+
+        conn.close()
+        return {"data": data}
+
+@app.post("/review")
+def post_review(review: Review):
+    with sem_db:
+        conn = sqlite3.connect(
+            "database/reviews.db",
+            check_same_thread=False
+        )
+        cur = conn.cursor()
+
+        cur.execute("""
+            INSERT INTO reviews (website, rating, text, created_at)
+            VALUES (?, ?, ?, ?)
+        """, (review.website, review.rating, review.text, review.date))
+
+        conn.commit()
+        conn.close()
+        return {"status": "ok"}
+
+@app.delete("/review")
+def delete_review(auth=Depends(get_api_key)):
+    with sem_db:
+        conn = sqlite3.connect(
+            "database/reviews.db",
+            check_same_thread=False
+        )
+        cur = conn.cursor()
+
+        cur.execute("""
+            DELETE FROM reviews;
+        """)
+
+        conn.commit()
+        conn.close()
+        return {"status": "ok"}